The proposed method used in this paper was inspired by the Pix2Pix, an image-to-image translation algorithm introduced by Phillip Isola et al. [1]}. Pix2Pix is the pseudonym for implementing a generic image-to-image translation solution that involves mapping pixels to pixels using CGANs. As mentioned earlier, GANs map a random noise vector to an output, while CGANs learn a mapping from an observation and random noise vector to the output. The framework used in [1]} differs from other CGANs frameworks because it was designed not to be application-specific like image-to-image translation methods. The main characteristics of Pix2Pix compared to CGAN is that its generator's input does not include random noise. This would make the output of the generator deterministic. To address this, the noise is added in the form of dropout layers to the generator's architecture.
Moreover, Pix2Pix chooses different architectures for its generator and discriminator, where U-Net [3]} and PatchGAN [4]} are used, respectively.
